Historical trends and multi-model ensemble forecasting

of extreme events

Dr. Caio A. S. Coelho

University of Reading, U.K.

E-mail: c.a.d.s.coelho@reading.ac.ukThanks to: David Stephenson, Mark New, Bruce Hewitson + Africa extremes workshop participants

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Talk plan

• What are extremes?

• Historical trend analysis of extremes in Africa

• What is going to happen to extremes in the future? - Extreme event forecasting

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What are extremes?

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Examples of wet and windy extremes

Extra-tropical cyclone

Hurricane

Polar low

Extra-tropical cyclone

Convective severe storm

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Examples of dry and hot extremesDrought

Wild fireDust storm

Dust storm

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IPCC 2001 definitionsSimple extremes:

“individual local weather variables exceeding critical levels on a continuous scale”

Complex extremes:“severe weather associated with particular climatic phenomena, often requiringa critical combination of variables”

Extreme weather event:“an event that would normally beas rare or rarer than the10th or 90th percentile.”

Extreme climate event:“an average of a number of weather events over a certain period of time which is itself extreme (e.g. rainfall over a season)”

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Some properties of extreme eventsSeverity large impacts (extreme losses):

– Injury and loss of life– Damage to the environment– Damage to ecosystems

Extremenesslarge values of meteorological variables:

– maxima or minima– exceedance above a high threshold– exceedance above all previous recorded values (record breaker)

Rarity/frequencysmall probability of occurrence

Longevity– Acute: Having a rapid onset and following a short but severe course– Chronic: Lasting for a long period of time (> 3 months) or marked by

frequent recurrence

9090thth percentile percentile

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Historical trend analysis of extremes in Africa

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Southern and West Africa workshop on weather and climate extremes

Cape Town, South Africa, 31May - 4 June 2004

Organization:

• Expert Team on Climate Change Detection Monitoring and Indices (ETCCDMI)

• WMO Commission of Climatology (CCI)

• Climate Variability and Predictability (CLIVAR) project

Aim: Derive indices from daily data to measure changes in extremes

Participants: 14 countries Data: 63 stations (1961-2000)

daily (minimun and maximum) temperature and precipitation

New, M., B. Hewitson, D. B. Stephenson, A. Tsiga, A. Kruger, A. Manhique, B. Gomez,C. A. S. Coelho, D. N. Masisi, E. Kululanga, E. Mbambalala, F. Adesina, H. Saleh, J. Kanyanga, J. Adosi, L. Bulane, L. Fortunata, M. L. Mdoka and R. Lajoie, 2005: Evidence of trends in daily climate extremes over Southern and West Africa,Submitted to J. Geophys. Res. (Atmospheres).

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Workshop methodologySoftware: RClimDex ( http://cccma.seos.uvic.ca/ETCCDMI/ )

Data quality control• negative precipitatoin• max. temp. < min. temp.• search for outliers based on threshold defined in terms of

standard deviation from the long-term (1961-2000) daily mean• visual inspection of time series plots

Computation of climate indices using RClimDex• 15 temperature indices• 10 precipitation indices

Trend estimation and interpretation of results

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Trends in temperature extreme indices

ColdColdTT<< 10th 10th

percentilepercentile

HotHotTT>> 90th 90th

percentilepercentile

MinimumMinimum MaximumMaximumCold night frequencyCold night frequency Cold day frequencyCold day frequency

Hot night frequencyHot night frequency Hot day frequencyHot day frequency

Source: New et al. 2005 (submitted to Source: New et al. 2005 (submitted to J. Geophys. Res. (Atmospheres).))

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Summary of findings for temperature extremes in Africa

Shift in the frequency distribution towards larger values

• Frequency of extremely cold days and nights has decreased• Frequency of extremely hot days and nights has increased

1010thth percentile percentile 9090thth percentile percentile

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Trends in precipitation indices

Annual total precipitationAnnual total precipitation

Max. nMax. noo of consec. dry days of consec. dry daysnnoo of days with prec. > 20 mm of days with prec. > 20 mm

Annual total precip. > 95Annual total precip. > 95thth perc. perc.

Longest dry spellLongest dry spell Very heavy precipitation day Very heavy precipitation day

Source: New et al. 2005 (submitted toSource: New et al. 2005 (submitted to J. Geophys. Res. (Atmospheres).))

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Summary of findings for precipitation indices in Africa

No trends found in many stations

Only a few stations show statistically significant trends

• Some stations are getting drier • Longest dry spells are getting longer for a few stations

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Suggestion for collaboration work

Perform similar extreme indices analysis for Cuban stations

Required tools:• RClimDex ( http://cccma.seos.uvic.ca/ETCCDMI/ )• R ( http://www.r-project.org/ )

(both are freely available)

Such study will allow us:• To identify how extremes behaved in the past in Cuba• To diagnose observed changes in extremes in Cuba• Compare results with findings of Caribbean climate and

weather extremes workshop held in Jamaica 2001

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What is going to happen to extremes in the future?

Extreme event forecasting

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ENSEMBLES: ENSEMBLE-based Predictions of Climate Changes and their Impacts

WP4.3: Understanding Extreme Weather and Climate

Events   Provision of statistical methods for identifying and forecasting extreme events

and the climate regimes with which they are associated. More robust assessments of the effects of climate change on the probability of extreme events and on the characteristics of natural modes of climate variability.

us!

How best to make probability forecasts of extremes?

multi-model ensemble tail probabilities

Need to develop:Multi-model calibration and combination approach for extremes

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Calibration and combination of multi-model ensemble

seasonal forecasts:

South American rainfall example

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Conceptual framework

)y(p

)x(p)x|y(p)y|x(p

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Data Assimilation “Forecast Assimilation”

)x(p

)y(p)y|x(p)x|y(p

f

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DJF rainfall anomalies for 1975/76 and 1982/83Obs Multi-model Forecast

Assimilation

(mm/day)

ACC=-0.09

ACC=0.32

ACC=0.59

ACC=0.56

La Nina1975/76

El Nino1982/83

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Summary of multi-model ensemble forecast calibration and combination

• Forecast assimilation: Unified framework for calibration and combination

• Useful approach for improving skill of South American rainfall seasonal forecasts

• Similar approach will be developed for extreme event forecasts in ENSEMBLES

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The EUROBRISA ProjectLead Investigator: Dr Caio Coelho

Key Idea: To improve seasonal forecasts in S. America:a region where there is seasonal forecast skill and useful value.

Aims• Strengthen collaboration and promote exchange of expertise and information between European and S. American seasonal forecasters

• Produce improved well-calibrated real-time probabilistic seasonal forecasts for South America

• Develop real-time forecast products for non-profitable governmental use (e.g. reservoir management, hydropower production, and agriculture)

EUROBRISA was approved by ECMWF council in June 2005

http://www.met.rdg.ac.uk/~swr01cac/EUROBRISA

Institutions Country Partners

CPTEC Brazil Coelho, Cavalcanti, Silva Dias, Pezzi

ECMWF EU Anderson, Balmaseda, Doblas-Reyes, Stockdale

INMET Brazil Moura, Silveira

Met Office UK Graham, Davey, Colman

Météo France France Déqué

SIMEPAR Brazil Guetter

Uni. of Reading UK Stephenson

Uni. of Sao Paulo Brazil Ambrizzi, Silva Dias

CIIFEN Ecuador Camacho, Santos

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Climate Analysis Group

http://www.met.reading.ac.uk/cag/

Aim: develop and apply statistical analysis techniques to improve both understanding and predictive capability of weather and climate variations

Main areas of interest: • climate modes and regimes e.g. NAO and Asian

Monsson• weather and climate extremes• Forecast verification, combination and calibration

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The End